Distributed versus Localist Representations: Evidence from a Study of Item Consistency in a Case of Classical Anomia

Abstract

Models of word production and comprehension can be split into two broad classes: localist and distributed. In localist architectures each word within the lexicon is represented by a single unit. The distributed approach, on the other hand, encodes each lexical item as a pattern of activation across a set of shared units. If we assume that the localist representations are more than a convenient shorthand for distributed representations at the neuroanatomical level, it should be possible to find patients who, after brain injury, have lost specific words from their premorbid vocabulary.Following a closed head injury, JS had severe word-finding difficulties with no measurable semantic impairment nor did he make phonological errors in naming. Cueing with an initial phoneme proved relatively ineffective. JS showed a high degree of item consistency across three administrations of two tests of naming to confrontation. This consistency could not be predicted from a linear combination of psycholinguistic variables but the distribution fitted a stochastic model in which it is assumed that a proportion of items have become consistently unavailable.Further evidence is presented which suggests that these items are not, in fact, lost but rather have a very low probability of retrieval. Given phonemic cueing of sufficient length, or delayed repetition priming from a written word, the consistently unnamed items were produced by JS. Additional data is reported which seems to support a distributed model of speech production. JS's naming accuracy for one set of pictures was found to predict his performance on a second set of items only when the names of the pictures were both semantically and phonologically related (e.g., cat–rat). There was no association for pairs of pictures if they were only semantically (e.g., cat–dog) or phonologically related (e.g., cat–cap).It is argued that JS's data are best described in terms of a graded, non-linear, distributed model of speech production.